Volume 29 Issue 6
Dec.  2020
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LYU Yanxia, LI Wenjie, WANG Yue, et al., “RMHSForest: Relative Mass and Half-Space Tree Based Forest for Anomaly Detection,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1093-1101, 2020, doi: 10.1049/cje.2020.09.010
Citation: LYU Yanxia, LI Wenjie, WANG Yue, et al., “RMHSForest: Relative Mass and Half-Space Tree Based Forest for Anomaly Detection,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1093-1101, 2020, doi: 10.1049/cje.2020.09.010

RMHSForest: Relative Mass and Half-Space Tree Based Forest for Anomaly Detection

doi: 10.1049/cje.2020.09.010
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  • Corresponding author: LYU Yanxia (corresponding author) received the Ph.D. degree in the School of Computer Science and Engineering, Northeastern University, Shenyang, China, in 2020. She is currently a lecturer with the School of Computer and Communication Engineering, Northeastern University at Qinhuangdao. She is a member of the CCF and ACM. Her current research interests include data mining, sentiment analysis and recommendation system. She has authored or co-authored 14 technical papers in journals, such as the Tsinghua Science and Technology, Advanced Engineering Informatics, Neural Computing & Applications. (Email:lyx@neuq.edu.cn)
  • Received Date: 2019-07-15
  • Publish Date: 2020-12-25
  • Anomaly detection refers to identify the true anomalies from a given data set. We present an ensemble anomaly detection method called Relative mass and half-space tree based forest (RMHSForest), which detect anomalies, including global and local anomalies, based on relative mass estimation and halfspace tree. Different from density or distance based measure, RMHSForest utilizes a novel relative mass estimation to improve the detection of local anomaly. Meanwhile, half-space tree based on augmented mass can estimate a mass distribution efficiently without density or distance calculations or clustering. Our empirical results show that RMHSForest outperforms the current popular anomaly detection algorithms in terms of AUC and processing time in the test data sets.
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